Monitoring devices are becoming increasingly popular in many industries. This use has only increased with the proliferation of mobile devices such as smartphones. Many monitoring devices today provide the ability to couple or wirelessly connect with mobile devices to provide increased insight to a user. At the same time, device size and cost have significantly decreased, leading to smaller monitoring devices that may combine multiple types of sensors to collect data.
Multiple industries are being transformed by monitoring devices. One example of an industry that is being transformed by such technology is the health and fitness industry. Exercise and physical fitness are beneficial for many reasons, including improved health, increased life span, and for simply staying in shape. People exercise for many reasons, including reducing stress, staying active, or to reach new milestones such as running a mile faster than ever before. To get the maximum benefit from physical movement, a person needs to be able to quickly, accurately and simply receive information concerning their exercise. Thus in the health and fitness industry, such sensors are being used to analyze metrics such as a user's heart rate, body temperature, and various indicators of stress. In other areas of health science, sensors are being used to analyze a patient's sleeping patterns, breathing rates, and movement in the night, in order to determine if the patient is in deep sleep, or if the patient is suffering from some sort of disrupted sleep such as an apnea.
Another industry that is being transformed by such monitoring devices is the mobile video industry. Photographers and moviemakers are always finding new ways to affix cameras and/or video equipment on any number of moving bodies in order to monitor and capture events from multiple viewing angles like never before.
Currently available monitoring devices usually comprise of a single fixture with either a singular or multiple sensors. A simple example would be that of pedometers, and similar wearable devices, that can track and calculate how many steps a user has walked based off the sensor readings on an internal MEMS inertial sensor. A similar monitoring device can be implemented to track other activities depending on where they are placed, and what type of sensor is included in them. With the addition of more sensors these monitoring devices can gather multiple types of data in one unit. For example some modern devices, such as digital cameras, now come with both photo sensors and GPS sensors—such devices can simultaneously collect image data and while also noting GPS location data. Such devices thus allow users to not only recall what the image sensor saw but also recall where device was located when it captured the said image.
The aforementioned monitoring devices have specific limitations with respect to use and analytic ability. While such singular devices may provide multiple accurate data points they are naturally limited to only collecting data from a single location which is thus the data has limited analytical use—in fitness monitoring devices this is typically this the case because there is usually only one wearable piece. One solution is to have a system that incorporates a plurality of monitoring devices so that the system may generate more data points from multiple locations and thus provide greater analytical use (e.g. a monitoring device on each arm of a user to measure relative activity and balance). However setting up and initializing multiple monitoring devices are cumbersome for users and thus makes such an approach unpopular.
In addition, setting up such system is a nontrivial matter. Since the data sets collected from each of the monitoring devices would be in the form of a time series it is imperative that all of the incoming data thus be time correlated. It is not enough to simply rely on the internal clocks of each monitoring device as most practical timing devices (e.g. crystal oscillators) tend to drift about 40 microseconds/second thus rendering such internal clocks unusable after a mere few minutes. Current state of the art systems and methods do not disclose how to time synchronize a plurality of monitoring devices—in order to correlate useful data—without implementing a costly “absolute clock,” such as a GPS, in each device. Presently available systems also fail to provide a method of automatically triggering all devices within a given topology.
Accordingly, there is a current and impending need for a system that can provide time synchronized data without requiring an absolute clock implemented in each monitoring device. There is also a need for a low-power system that can provide robust and highly accurate monitoring data that can be coupled with a mobile device for providing output regarding a given activity.